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Comparison Member and Candidate Countries to the European Union by Means of Main Health Indicators∗

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Comparison Member and Candidate Countries to the European Union by Means of Main Health Indicators∗ China-USA Business Review, ISSN 1537-1514 April 2012, Vol. 11, No. 4, 556-563 D D AV I D PUBLISHING Comparison Member and Candidate Countries to the European ∗ Union by Means of Main Health Indicators Fatma Lorcu Trakya University, Edirne, Turkey Bilge Acar Bolat Istanbul University, Istanbul, Turkey Throughout history, health policies and the institutionalization of health care have taken shape according to political and economic conditions, social structures and value systems of societies, as well as to their needs and changes in health conditions. Today, economic development is addressed from a different approach in which the issue of health plays an important part. With a focus on the health sector’s role in development, this new approach has increased the importance of this sector of government and of life, allowing data on health to be included in many countries’ development indicators. The aim of this study is to identify the differences between EU former member states, member states, and candidate countries (including Turkey) in terms of health indicators that have become the key indicators of social and economic development. In this study, discriminant analysis will be used in order to analyse the differences between the European Union (EU) countries, and candidate countries in terms of main health indicators. Under-five mortality rate (‰), health expenditure per capita (PPP US$), total expenditure on health as percent of gross domestic product expenditure (percentage of GDP), immunization coverage among one-year-olds with one dose of measles (%), prevalence of current tobacco use among adults (≥ 15 years) (%), per capita recorded alcohol consumption (litres of pure alcohol) among adults, total fertility rate (%), hospital beds (per 1,000 population), physicians (per 1,000 population) and life expectancy are all included in the analysis as main health indicators of a country’s fitness for acceptance into the EU. As a result of the analysis, the discriminant variables that determine the statuses of former member, member, and candidate countries in terms of health indicators are identified “health expenditures per capita” and “life expectancy”. Another striking result is that Romania and Bulgaria, which became full member states in 2007, had similar health indicators to Turkey’s in 2004. Keywords: health indicators, discriminant analysis, European Union, Turkey Introduction The European Union agreed on final arrangements to enable Bulgaria and Romania to join in 2007. This raised ∗ An earlier version of this paper was presented at 12th International Symposium on Econometrics Operations Research and Statistics in Denizli/Turkey, May 2011. Fatma Lorcu, Ph.D., Assistant Professor, Faculty of Economics and Administrative Sciences, Trakya University. Bilge Acar Bolat, Ph.D., Research Assistant, School of Business, Istanbul University. Correspondence concerning this article should be addressed to Bilge Acar Bolat, School of Business, Istanbul University, Avcılar, Istanbul, Turkey. E-mail: [email protected]; [email protected]. COMPARISON MEMBER AND CANDIDATE COUNTRIES TO THE EUROPEAN UNION 557 the number of members from 25 to 27. Romania and Bulgaria submitted their formal applications for membership of the EU in 1995. Turkey is still an official candidate although Turkey applied for associate membership in the European Economic Community in 1963, and the earliest date that Turkey could enter the EU is 2013. In contrast to Romania’s and Bulgaria’s short and painless admission to the Union, Turkey, which in fact is comparable to those countries, still has to live through a long-haul process. On the other hand, “choosing adequate strategies for the future of countries in an increasingly globalise world”, Turkey and its variety of sectors have to “find its places among other countries”, and in this respect knowledge about the influential general factors is one of the most important aspects of the discussion (Ay, 2007, p. 4). One of the most important of these sectors is the health sector, which is in fact gaining importance day by day and is an indicator of socio-economic development. One is likely to come across some literature dealing with the significance of health indicators, while making important comparisons between Turkey and other countries. In this context Gauld, Ikegami, Barr, Chiang, Gould, and Kwon (2006) discusses health systems in economically developed Asian countries, while Vehid (2000), Yıldırım (2004), Kisa, Younis, and Kisa (2007) and Pacifico (2004) compare the health systems in a number of EU countries. On the other hand, Bal and Orkcu (2005) analyse the socio-economic indicators of EU members, and those chosen to become members, by using a newly developed categorisation technique which was based on discriminate analysis and data envelopment analysis. Our study has two goals. The first is to analyse whether health status differences exists between EU former members, current members, and candidates, and if so. The second is to find out if health status has a significant association with becoming an EU member. In order to achieve these goals, member and candidate countries are classified into three groups: former member—current member—future member; when dealing with issues of health, it will be outlined which variables current EU member states differ from those of the future. The analysis will use the 2004 findings for the 28 concerned countries. The reason for the choice of material from 2004 is that Turkey, Romania and Bulgaria at that time were all given the future member state status. In this respect, the analysis will be able to assess and question the validity of the EU’s decisions with regards to the grouping of these countries. It is a well-known fact that Bulgaria and Romania became EU members in 2007, with less regulation to comply with, despite the fact that their future member status was granted later than in the case of Turkey. The research will compare the three countries and their individual general health situations in 2004 when all were regarded as future member states, and it will reveal if Turkey’s situation was any different. The data used in this paper is cross sectional data set covering 28 countries, of which 25 are EU member states, two are due to join the EU in 2007 (Bulgaria and Romania) and one is a potential member state (Turkey). The data is from the OECD Health Data 2004 for OECD countries, and from the WHO (European health for all database) for non-OECD countries. Section two explains our methodology and techniques. Section three summarizes definitions of variables which are used in the analysis by literature. Section four concludes the paper by analyzing results and developing suggestions. Method In order to meet objectives identified above, two methods are applied: Logistic regression and discriminant analysis. However, since normality of independent variables among groups is present and an assumption of 558 COMPARISON MEMBER AND CANDIDATE COUNTRIES TO THE EUROPEAN UNION equality between group variance-covariance matrices is provided, using the discriminant analysis method is considered to be appropriate. With the help of the discriminant analysis the following can be determined: The difference amongst groups within variable sets; The identification of variables which determine the differences between groups; The contribution of distinctive variables to the function; The percent of variance in the dependent variable explained by independent; The level of groups’ membership prediction accuracy (Hair, Anderson, Tatham, & Black, 1998, p. 256). The discriminant function can be written as follows: D = b1x1 + b2x2 + ... + bnxn + c the b’s represent the discriminant coefficients, the x’s the discriminating (independent) variables and the c’s the constants. Regression equations are similar to the equation, but here the b coefficients specify the coefficients which maximize the distance between the means of the dependent variable. In addition, dependent variable is different in regression analysis where the coefficients D functions as a categorical (Garson, 2009). In addition, unlike regression analysis, dependent variable is categorical in the discriminant analysis. Variables Used in the Analysis The independent variables of the research were chosen to be the following. Under-five mortality rate (probability of dying by age five per 1,000 live births) (x1), number of physicians (per 1,000 population) (x2), health expenditure per capita (PP US$) (x3), total expenditure on health as % of gross domestic product expenditure (%) (x4), immunization coverage among one-years-olds with one dose of measles (%) (x5), number of hospital beds per 1,000 people (x6), per capita recorded alcohol consumption (litres of pure alcohol) among adults (x7), fertility rate (%) (x8), prevalence of current tobacco use among adults (≥ 15 years) (%) (x9), life expectancy (year) (x10). These variables will be explained in further detail below. Child mortality is one of the most crucial and avoidable global health concerns (Moser, Leon, & Gwatkin, 2005, p. 1180). Under-five mortality rate estimates the number of new-born babies that will die before reaching their fifth birthday, based on current age-specific mortality rates for each country. “It provides a key baseline indication of how a country is progressing with its plan to realise children’s rights, in particular their rights to life, health-care services, nutrition, water, social security and protection” (Children’s Institute, 2006). Therefore, the mortality rate of under the age of five is adopted as an indicator for not only for health but also for development in general. This indicator was relied heavily as evidence of socio-economic development in the research conducted by Anderson Romani, Phillips, and Zyl (2002), Sen (1998), Chung and Muntaner (2006), and Moğultay (2005). The total fertility rate (TFR) is a more direct measure of the level of fertility than the crude birth rate, since it refers to births per woman. This indicator shows the potential for population change in the country. Tracking trends in fertility and birth rates helps support effective social planning and the allocation of basic resources across generations.
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